ANALISIS SENTIMEN OPINI PUBLIK MENGENAI SARANA DAN TRANSPORTASI MUDIK TAHUN 2019 PADA TWITTER MENGGUNAKAN ALGORITMA NAÏVE BAYES, NEURAL NETWORK, KNN DAN SVM
Various moments are immortalized in social networking, one of them Twitter. Twitter is one of the social media that allows users to interact, share information, or even to express feelings and opinions as well as in expressing opinions about "Mudik". Every tweets not all contain the same value, therefore there needs to be a sentiment analysis and tweet classification that discusses about to know the amount of public sentiment to mudik year 2019. This will be a concern for “facilicies transportation for goinghome” next year. One of method used is classification method using k-NN, SVM, Naïve Bayes and Neural Network. The results of this study can be seen that positive sentiments appear more than the value of other sentiments for Mudik 2019, next time negative’s opinion appear more than positive Mudik 2019. and k-NN algorithm has a higher accuracy, it is accuracy=90.76 and AUC=0.939, SVM generated accuracy=89.03% and AUC=0.500, Naïve Bayes generated accuracy=78.16% and AUC=0.567 then Neural Network generated accuracy=52.73% and AUC=0.000.
Aggarwal, C. C., & Zhai, C. X. (2013). Mining text data. Mining Text Data, 9781461432, 1–522. https://doi.org/10.1007/978-1-4614-3223-4
Fink, C. R., Chou, D. S., Kopecky, J. J., & Llorens, A. J. (2011). Coarse- and fine-grained sentiment analysis of social media text. Johns Hopkins APL Technical Digest (Applied Physics Laboratory), 30(1), 22–30.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/https://doi.org/10.2200/S00416ED1V01Y201204HLT016
Pertiwi, M. W. (2019). Laporan Akhir Penelitian. STMIK Nusa Mandiri.
Simpson, M. S., & Demner-Fushman, D. (2012). Biomedical Text Mining: A Survey of Recent Progress. (C. C. Aggarwal & C. Zhai, Eds.), Mining Text Data. New York: Springer. https://doi.org/10.1007/978-1-4614-3223-4
Twitter. (2019). Mudik. Retrieved from https://twitter.com/search?q=mudik&src=typd
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